Finding the country- and age-specific optimal vaccination strategy (Spring 2021)
We all are experiencing the severity of the current COVID-19 pandemic and are eagerly waiting for an effective vaccine. Together with a few other collaborators from different institutions, we are currently working on two models of COVID-19 transmission dynamics that incorporate the presence of a potentially not fully protective vaccine and predict the effect of such a vaccine on the further course of the epidemic. One model is a compartmental age-structured SIR (Susceptible-Infectious-Recovered) type ODE (Ordinary Differential Equations) model, while the other is a network model. In our current models, we consider country- and age-specific susceptibility, mortality, and contact rates. The effect of vaccine efficacy and coverage, as well as social distancing practiced by those vaccinated and those not vaccinated are also considered.
In the Spring semester, we will work with the interested students on extensions of these models. Students will become familiar with some basic concepts of Mathematical Biology, in particular the two modeling frameworks (ODE and network models) as well as statistical data fitting techniques. They will collect data, conduct descriptive analysis, and parametrize the models. Using available country- and age-specific data, the students will compare the predictions made by the two different models and identify optimal vaccination strategies for each country, i.e., which age groups to vaccinate first. In addition, the students will be encouraged to come up with creative questions that we may be able to answer using ODE and/or network models.
For more information contact Md Rafiul Islam (rafiul@iastate.edu).
People:
Md Rafiul Islam (Postdoc)
Claus Kadelka (Faculty)
Audrey McCombs (Grad; stat)
Jake Alston (Undergrad)
Kassandra Chino-Gonzalez (Undergrad)
Caroly Coronado-Vargas (Undergrad)
Noah Morton (Undergrad)
Emma Staut (Undergrad)
Benjamin Studebaker (Undergrad)
Pre-requisites:
Programing (Matlab/R/Python) is desirable but not required
Experience with differential equations (Math 266/267) and applied statistics (at the level of an introductory statistics course) are desirable, but not required